Traditional methods on segmentation and classification of synthetic aperture radar (SAR) imagery may have failed to take advantage of nuances of the complex data; instead, they have only focused on detected imagery. As a result, imagery segmentation systems in traditional methods require human intervention to clean up boundaries or verify correctness. Since segmentation is a prerequisite for terrain classification, these early segmentation errors propagate downstream. Consumers of classified terrain regions, such as automated target recognition (ATR), may miss crucial search areas. The traditional methods may not be resilient against noisy or distorted inputs.
Furthermore, the traditional methods of segmentation and classification of SAR imagery may not be adaptable to new radar instrumentation. It is common for machine learning systems to require days or weeks of training before beginning to produce accurate results. Once the system has been trained on the data from one sensor, adapting to a new sensor will commonly require another large batch of labeled training data. Depending on circumstances, this training data may not exist or may be prohibitively expensive.